Overview

Dataset statistics

Number of variables25
Number of observations10142
Missing cells4
Missing cells (%)< 0.1%
Duplicate rows14
Duplicate rows (%)0.1%
Total size in memory4.1 MiB
Average record size in memory428.5 B

Variable types

Text3
Categorical1
Numeric21

Alerts

Dataset has 14 (0.1%) duplicate rowsDuplicates
Ave is highly overall correlated with Cum Ave and 5 other fieldsHigh correlation
Ct is highly overall correlated with Cum Inns Total and 3 other fieldsHigh correlation
Cum Ave is highly overall correlated with Ave and 6 other fieldsHigh correlation
Cum Inns Total is highly overall correlated with Ct and 7 other fieldsHigh correlation
Cum Runs Total is highly overall correlated with Ave and 8 other fieldsHigh correlation
Cum SR is highly overall correlated with Ave and 5 other fieldsHigh correlation
Cumulative Econ is highly overall correlated with Cumulative Inns and 6 other fieldsHigh correlation
Cumulative Inns is highly overall correlated with Cumulative Econ and 6 other fieldsHigh correlation
Cumulative Overs is highly overall correlated with Cumulative Econ and 6 other fieldsHigh correlation
Cumulative Runs is highly overall correlated with Cumulative Econ and 6 other fieldsHigh correlation
Cumulative Wkts is highly overall correlated with Cumulative Econ and 6 other fieldsHigh correlation
D/I is highly overall correlated with Ct and 1 other fieldsHigh correlation
Dis is highly overall correlated with Ct and 3 other fieldsHigh correlation
Econ is highly overall correlated with Cumulative Econ and 5 other fieldsHigh correlation
Inns is highly overall correlated with Ave and 5 other fieldsHigh correlation
Mat is highly overall correlated with Cum Inns Total and 2 other fieldsHigh correlation
Overs is highly overall correlated with Cumulative Econ and 6 other fieldsHigh correlation
Runs is highly overall correlated with Ave and 7 other fieldsHigh correlation
SR is highly overall correlated with Ave and 4 other fieldsHigh correlation
Wkts is highly overall correlated with Cumulative Econ and 5 other fieldsHigh correlation
Inns has 913 (9.0%) zerosZeros
Runs has 1461 (14.4%) zerosZeros
SR has 1461 (14.4%) zerosZeros
Ave has 1461 (14.4%) zerosZeros
Cum Ave has 762 (7.5%) zerosZeros
Cum Runs Total has 762 (7.5%) zerosZeros
Cum Inns Total has 438 (4.3%) zerosZeros
Cum SR has 762 (7.5%) zerosZeros
Overs has 3582 (35.3%) zerosZeros
Wkts has 4730 (46.6%) zerosZeros
Econ has 3581 (35.3%) zerosZeros
Cumulative Overs has 2943 (29.0%) zerosZeros
Cumulative Wkts has 3743 (36.9%) zerosZeros
Cumulative Runs has 2942 (29.0%) zerosZeros
Cumulative Inns has 2942 (29.0%) zerosZeros
Cumulative Econ has 2942 (29.0%) zerosZeros
Dis has 1858 (18.3%) zerosZeros
Ct has 1880 (18.5%) zerosZeros
St has 9333 (92.0%) zerosZeros
D/I has 1854 (18.3%) zerosZeros

Reproduction

Analysis started2024-10-28 06:47:19.528291
Analysis finished2024-10-28 06:49:46.107833
Duration2 minutes and 26.58 seconds
Software versionydata-profiling v4.8.3
Download configurationconfig.json

Variables

Distinct3044
Distinct (%)30.0%
Missing0
Missing (%)0.0%
Memory size643.9 KiB
2024-10-28T12:19:46.681836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters81136
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique921 ?
Unique (%)9.1%

Sample

1st row1d45c01a
2nd row1d45c01a
3rd row321be7e3
4th row58c2fac4
5th row6ef13460
ValueCountFrequency (%)
67a9fe72 24
 
0.2%
3d5d51a5 24
 
0.2%
249d60c9 19
 
0.2%
99b75528 18
 
0.2%
8a75e999 17
 
0.2%
0a8fce53 17
 
0.2%
ffe699c0 17
 
0.2%
62af8546 16
 
0.2%
3ff033bb 16
 
0.2%
1212cbec 16
 
0.2%
Other values (3034) 9958
98.2%
2024-10-28T12:19:47.423265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
d 5286
 
6.5%
e 5253
 
6.5%
b 5227
 
6.4%
a 5219
 
6.4%
c 5132
 
6.3%
9 5108
 
6.3%
7 5086
 
6.3%
3 5081
 
6.3%
2 5071
 
6.2%
1 5045
 
6.2%
Other values (6) 29628
36.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 81136
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
d 5286
 
6.5%
e 5253
 
6.5%
b 5227
 
6.4%
a 5219
 
6.4%
c 5132
 
6.3%
9 5108
 
6.3%
7 5086
 
6.3%
3 5081
 
6.3%
2 5071
 
6.2%
1 5045
 
6.2%
Other values (6) 29628
36.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 81136
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
d 5286
 
6.5%
e 5253
 
6.5%
b 5227
 
6.4%
a 5219
 
6.4%
c 5132
 
6.3%
9 5108
 
6.3%
7 5086
 
6.3%
3 5081
 
6.3%
2 5071
 
6.2%
1 5045
 
6.2%
Other values (6) 29628
36.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 81136
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
d 5286
 
6.5%
e 5253
 
6.5%
b 5227
 
6.4%
a 5219
 
6.4%
c 5132
 
6.3%
9 5108
 
6.3%
7 5086
 
6.3%
3 5081
 
6.3%
2 5071
 
6.2%
1 5045
 
6.2%
Other values (6) 29628
36.5%

Player
Text

Distinct3024
Distinct (%)29.8%
Missing0
Missing (%)0.0%
Memory size668.8 KiB
2024-10-28T12:19:48.154809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length19
Mean length10.517945
Min length5

Characters and Unicode

Total characters106673
Distinct characters55
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique909 ?
Unique (%)9.0%

Sample

1st rowA Andrews
2nd rowA Andrews
3rd rowA Ashok
4th rowA Athanaze
5th rowA Balbirnie
ValueCountFrequency (%)
s 312
 
1.5%
khan 256
 
1.2%
a 243
 
1.2%
r 206
 
1.0%
m 205
 
1.0%
ali 197
 
1.0%
d 182
 
0.9%
t 178
 
0.9%
singh 170
 
0.8%
mohammad 162
 
0.8%
Other values (3293) 18507
89.8%
2024-10-28T12:19:49.247406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 13686
 
12.8%
10476
 
9.8%
i 5735
 
5.4%
e 5532
 
5.2%
n 5311
 
5.0%
r 4914
 
4.6%
h 4806
 
4.5%
l 3538
 
3.3%
o 3386
 
3.2%
m 3320
 
3.1%
Other values (45) 45969
43.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 106673
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 13686
 
12.8%
10476
 
9.8%
i 5735
 
5.4%
e 5532
 
5.2%
n 5311
 
5.0%
r 4914
 
4.6%
h 4806
 
4.5%
l 3538
 
3.3%
o 3386
 
3.2%
m 3320
 
3.1%
Other values (45) 45969
43.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 106673
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 13686
 
12.8%
10476
 
9.8%
i 5735
 
5.4%
e 5532
 
5.2%
n 5311
 
5.0%
r 4914
 
4.6%
h 4806
 
4.5%
l 3538
 
3.3%
o 3386
 
3.2%
m 3320
 
3.1%
Other values (45) 45969
43.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 106673
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 13686
 
12.8%
10476
 
9.8%
i 5735
 
5.4%
e 5532
 
5.2%
n 5311
 
5.0%
r 4914
 
4.6%
h 4806
 
4.5%
l 3538
 
3.3%
o 3386
 
3.2%
m 3320
 
3.1%
Other values (45) 45969
43.1%
Distinct97
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size648.7 KiB
2024-10-28T12:19:49.812248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length14
Mean length8.4854072
Min length4

Characters and Unicode

Total characters86059
Distinct characters51
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSwitzerland
2nd rowSwitzerland
3rd rowNew Zealand
4th rowWest Indies
5th rowIreland
ValueCountFrequency (%)
new 455
 
3.5%
india 380
 
2.9%
pakistan 330
 
2.5%
indies 326
 
2.5%
south 326
 
2.5%
west 326
 
2.5%
sri 325
 
2.5%
lanka 325
 
2.5%
zimbabwe 319
 
2.5%
africa 308
 
2.4%
Other values (103) 9595
73.7%
2024-10-28T12:19:50.735161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 13977
16.2%
n 7438
 
8.6%
i 6474
 
7.5%
e 6184
 
7.2%
r 4022
 
4.7%
t 3776
 
4.4%
l 3640
 
4.2%
d 3560
 
4.1%
s 3438
 
4.0%
2873
 
3.3%
Other values (41) 30677
35.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 86059
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 13977
16.2%
n 7438
 
8.6%
i 6474
 
7.5%
e 6184
 
7.2%
r 4022
 
4.7%
t 3776
 
4.4%
l 3640
 
4.2%
d 3560
 
4.1%
s 3438
 
4.0%
2873
 
3.3%
Other values (41) 30677
35.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 86059
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 13977
16.2%
n 7438
 
8.6%
i 6474
 
7.5%
e 6184
 
7.2%
r 4022
 
4.7%
t 3776
 
4.4%
l 3640
 
4.2%
d 3560
 
4.1%
s 3438
 
4.0%
2873
 
3.3%
Other values (41) 30677
35.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 86059
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 13977
16.2%
n 7438
 
8.6%
i 6474
 
7.5%
e 6184
 
7.2%
r 4022
 
4.7%
t 3776
 
4.4%
l 3640
 
4.2%
d 3560
 
4.1%
s 3438
 
4.0%
2873
 
3.3%
Other values (41) 30677
35.6%

Season
Categorical

Distinct21
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size618.5 KiB
2024
1255 
2022
1067 
2023
926 
2023/24
886 
2021/22
823 
Other values (16)
5185 

Length

Max length7
Median length4
Mean length5.4349241
Min length4

Characters and Unicode

Total characters55121
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021/22
2nd row2022
3rd row2023
4th row2024
5th row2015

Common Values

ValueCountFrequency (%)
2024 1255
12.4%
2022 1067
10.5%
2023 926
9.1%
2023/24 886
8.7%
2021/22 823
 
8.1%
2022/23 791
 
7.8%
2019/20 694
 
6.8%
2019 628
 
6.2%
2021 604
 
6.0%
2018/19 363
 
3.6%
Other values (11) 2105
20.8%

Length

2024-10-28T12:19:51.029375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2024 1255
12.4%
2022 1067
10.5%
2023 926
9.1%
2023/24 886
8.7%
2021/22 823
 
8.1%
2022/23 791
 
7.8%
2019/20 694
 
6.8%
2019 628
 
6.2%
2021 604
 
6.0%
2018/19 363
 
3.6%
Other values (11) 2105
20.8%

Most occurring characters

ValueCountFrequency (%)
2 23388
42.4%
0 11179
20.3%
1 6083
 
11.0%
/ 4851
 
8.8%
3 2603
 
4.7%
4 2460
 
4.5%
9 1685
 
3.1%
5 911
 
1.7%
8 741
 
1.3%
6 685
 
1.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 55121
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 23388
42.4%
0 11179
20.3%
1 6083
 
11.0%
/ 4851
 
8.8%
3 2603
 
4.7%
4 2460
 
4.5%
9 1685
 
3.1%
5 911
 
1.7%
8 741
 
1.3%
6 685
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 55121
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 23388
42.4%
0 11179
20.3%
1 6083
 
11.0%
/ 4851
 
8.8%
3 2603
 
4.7%
4 2460
 
4.5%
9 1685
 
3.1%
5 911
 
1.7%
8 741
 
1.3%
6 685
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 55121
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 23388
42.4%
0 11179
20.3%
1 6083
 
11.0%
/ 4851
 
8.8%
3 2603
 
4.7%
4 2460
 
4.5%
9 1685
 
3.1%
5 911
 
1.7%
8 741
 
1.3%
6 685
 
1.2%

Mat
Real number (ℝ)

HIGH CORRELATION 

Distinct24
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.0649773
Minimum1
Maximum24
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size79.4 KiB
2024-10-28T12:19:51.293070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median4
Q37
95-th percentile12
Maximum24
Range23
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.5190353
Coefficient of variation (CV)0.69477809
Kurtosis2.3297016
Mean5.0649773
Median Absolute Deviation (MAD)2
Skewness1.3856348
Sum51369
Variance12.383609
MonotonicityNot monotonic
2024-10-28T12:19:51.652194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
3 1602
15.8%
4 1400
13.8%
2 1336
13.2%
5 1155
11.4%
1 1130
11.1%
6 876
8.6%
7 619
 
6.1%
8 498
 
4.9%
9 374
 
3.7%
10 308
 
3.0%
Other values (14) 844
8.3%
ValueCountFrequency (%)
1 1130
11.1%
2 1336
13.2%
3 1602
15.8%
4 1400
13.8%
5 1155
11.4%
6 876
8.6%
7 619
 
6.1%
8 498
 
4.9%
9 374
 
3.7%
10 308
 
3.0%
ValueCountFrequency (%)
24 5
 
< 0.1%
23 3
 
< 0.1%
22 3
 
< 0.1%
21 8
 
0.1%
20 14
 
0.1%
19 10
 
0.1%
18 30
0.3%
17 35
0.3%
16 50
0.5%
15 70
0.7%

Inns
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct25
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.7316111
Minimum0
Maximum24
Zeros913
Zeros (%)9.0%
Negative0
Negative (%)0.0%
Memory size79.4 KiB
2024-10-28T12:19:51.926536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q35
95-th percentile10
Maximum24
Range24
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.2176947
Coefficient of variation (CV)0.86228029
Kurtosis3.0326555
Mean3.7316111
Median Absolute Deviation (MAD)2
Skewness1.5381669
Sum37846
Variance10.353559
MonotonicityNot monotonic
2024-10-28T12:19:52.184593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
1 1774
17.5%
2 1658
16.3%
3 1544
15.2%
4 1193
11.8%
0 913
9.0%
5 869
8.6%
6 592
 
5.8%
7 394
 
3.9%
8 324
 
3.2%
9 221
 
2.2%
Other values (15) 660
 
6.5%
ValueCountFrequency (%)
0 913
9.0%
1 1774
17.5%
2 1658
16.3%
3 1544
15.2%
4 1193
11.8%
5 869
8.6%
6 592
 
5.8%
7 394
 
3.9%
8 324
 
3.2%
9 221
 
2.2%
ValueCountFrequency (%)
24 1
 
< 0.1%
23 2
 
< 0.1%
22 1
 
< 0.1%
21 1
 
< 0.1%
20 4
 
< 0.1%
19 5
 
< 0.1%
18 15
 
0.1%
17 18
 
0.2%
16 29
0.3%
15 45
0.4%

Runs
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct450
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean58.100079
Minimum0
Maximum749
Zeros1461
Zeros (%)14.4%
Negative0
Negative (%)0.0%
Memory size79.4 KiB
2024-10-28T12:19:52.440782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15
median26
Q378
95-th percentile226
Maximum749
Range749
Interquartile range (IQR)73

Descriptive statistics

Standard deviation81.627696
Coefficient of variation (CV)1.4049498
Kurtosis8.0924354
Mean58.100079
Median Absolute Deviation (MAD)25
Skewness2.4866284
Sum589251
Variance6663.0807
MonotonicityNot monotonic
2024-10-28T12:19:52.702996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1461
 
14.4%
1 349
 
3.4%
2 282
 
2.8%
4 219
 
2.2%
5 207
 
2.0%
3 194
 
1.9%
6 191
 
1.9%
7 174
 
1.7%
8 150
 
1.5%
10 146
 
1.4%
Other values (440) 6769
66.7%
ValueCountFrequency (%)
0 1461
14.4%
1 349
 
3.4%
2 282
 
2.8%
3 194
 
1.9%
4 219
 
2.2%
5 207
 
2.0%
6 191
 
1.9%
7 174
 
1.7%
8 150
 
1.5%
9 143
 
1.4%
ValueCountFrequency (%)
749 1
< 0.1%
669 1
< 0.1%
625 1
< 0.1%
612 1
< 0.1%
608 1
< 0.1%
598 1
< 0.1%
597 2
< 0.1%
595 1
< 0.1%
587 1
< 0.1%
575 1
< 0.1%

SR
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct2998
Distinct (%)29.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean90.882747
Minimum0
Maximum600
Zeros1461
Zeros (%)14.4%
Negative0
Negative (%)0.0%
Memory size79.4 KiB
2024-10-28T12:19:53.114984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q151.2875
median97.68
Q3127.67
95-th percentile174.622
Maximum600
Range600
Interquartile range (IQR)76.3825

Descriptive statistics

Standard deviation57.223108
Coefficient of variation (CV)0.62963664
Kurtosis2.6886639
Mean90.882747
Median Absolute Deviation (MAD)35.65
Skewness0.48161086
Sum921732.82
Variance3274.484
MonotonicityNot monotonic
2024-10-28T12:19:53.453388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1461
 
14.4%
100 422
 
4.2%
50 256
 
2.5%
66.66 143
 
1.4%
33.33 142
 
1.4%
133.33 87
 
0.9%
75 83
 
0.8%
150 75
 
0.7%
125 70
 
0.7%
200 61
 
0.6%
Other values (2988) 7342
72.4%
ValueCountFrequency (%)
0 1461
14.4%
5.55 1
 
< 0.1%
5.88 1
 
< 0.1%
6.66 1
 
< 0.1%
7.14 2
 
< 0.1%
7.31 1
 
< 0.1%
8.33 4
 
< 0.1%
8.69 1
 
< 0.1%
10 5
 
< 0.1%
10.34 1
 
< 0.1%
ValueCountFrequency (%)
600 3
 
< 0.1%
400 11
0.1%
360 1
 
< 0.1%
350 6
 
0.1%
333.33 5
 
< 0.1%
328.57 1
 
< 0.1%
306.66 1
 
< 0.1%
300 15
0.1%
288.88 1
 
< 0.1%
285.71 1
 
< 0.1%

Ave
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1232
Distinct (%)12.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.982149
Minimum0
Maximum224
Zeros1461
Zeros (%)14.4%
Negative0
Negative (%)0.0%
Memory size79.4 KiB
2024-10-28T12:19:53.741836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median10.75
Q321.5
95-th percentile45
Maximum224
Range224
Interquartile range (IQR)18.5

Descriptive statistics

Standard deviation16.5175
Coefficient of variation (CV)1.1024786
Kurtosis13.301402
Mean14.982149
Median Absolute Deviation (MAD)8.75
Skewness2.5528863
Sum151948.96
Variance272.8278
MonotonicityNot monotonic
2024-10-28T12:19:54.089434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1461
 
14.4%
1 343
 
3.4%
2 312
 
3.1%
4 212
 
2.1%
3 212
 
2.1%
5 211
 
2.1%
6 177
 
1.7%
7 174
 
1.7%
8 154
 
1.5%
9 147
 
1.4%
Other values (1222) 6739
66.4%
ValueCountFrequency (%)
0 1461
14.4%
0.25 1
 
< 0.1%
0.33 9
 
0.1%
0.5 74
 
0.7%
0.6 1
 
< 0.1%
0.66 10
 
0.1%
0.67 1
 
< 0.1%
0.75 4
 
< 0.1%
1 343
 
3.4%
1.2 2
 
< 0.1%
ValueCountFrequency (%)
224 1
< 0.1%
213 1
< 0.1%
211 1
< 0.1%
155 1
< 0.1%
142 1
< 0.1%
138.33 1
< 0.1%
136 1
< 0.1%
134 1
< 0.1%
128 1
< 0.1%
126 1
< 0.1%

Cum Ave
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct3033
Distinct (%)29.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.599967
Minimum0
Maximum126
Zeros762
Zeros (%)7.5%
Negative0
Negative (%)0.0%
Memory size79.4 KiB
2024-10-28T12:19:54.424239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15
median13.2
Q322.79
95-th percentile39.9975
Maximum126
Range126
Interquartile range (IQR)17.79

Descriptive statistics

Standard deviation13.365325
Coefficient of variation (CV)0.8567534
Kurtosis4.5373361
Mean15.599967
Median Absolute Deviation (MAD)8.695
Skewness1.5184293
Sum158214.87
Variance178.63192
MonotonicityNot monotonic
2024-10-28T12:19:54.773560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 762
 
7.5%
1 234
 
2.3%
2 195
 
1.9%
5 139
 
1.4%
4 131
 
1.3%
3 123
 
1.2%
6 112
 
1.1%
7 97
 
1.0%
8 81
 
0.8%
10 72
 
0.7%
Other values (3023) 8196
80.8%
ValueCountFrequency (%)
0 762
7.5%
0.17 1
 
< 0.1%
0.2 1
 
< 0.1%
0.25 3
 
< 0.1%
0.29 1
 
< 0.1%
0.33 11
 
0.1%
0.38 1
 
< 0.1%
0.4 4
 
< 0.1%
0.43 1
 
< 0.1%
0.44 1
 
< 0.1%
ValueCountFrequency (%)
126 1
< 0.1%
117 1
< 0.1%
116.67 1
< 0.1%
115 1
< 0.1%
112.89 1
< 0.1%
106 1
< 0.1%
104 1
< 0.1%
98 1
< 0.1%
96 1
< 0.1%
95.57 1
< 0.1%

Cum Runs Total
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1239
Distinct (%)12.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean212.88464
Minimum0
Maximum4145
Zeros762
Zeros (%)7.5%
Negative0
Negative (%)0.0%
Memory size79.4 KiB
2024-10-28T12:19:55.116694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q115
median70
Q3249
95-th percentile943
Maximum4145
Range4145
Interquartile range (IQR)234

Descriptive statistics

Standard deviation361.90041
Coefficient of variation (CV)1.6999837
Kurtosis16.522216
Mean212.88464
Median Absolute Deviation (MAD)66
Skewness3.4345442
Sum2159076
Variance130971.9
MonotonicityNot monotonic
2024-10-28T12:19:55.472383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 762
 
7.5%
1 238
 
2.3%
2 212
 
2.1%
5 162
 
1.6%
6 131
 
1.3%
4 130
 
1.3%
7 124
 
1.2%
3 124
 
1.2%
8 122
 
1.2%
10 105
 
1.0%
Other values (1229) 8032
79.2%
ValueCountFrequency (%)
0 762
7.5%
1 238
 
2.3%
2 212
 
2.1%
3 124
 
1.2%
4 130
 
1.3%
5 162
 
1.6%
6 131
 
1.3%
7 124
 
1.2%
8 122
 
1.2%
9 85
 
0.8%
ValueCountFrequency (%)
4145 1
< 0.1%
3698 1
< 0.1%
3492 1
< 0.1%
3485 1
< 0.1%
3355 1
< 0.1%
3313 1
< 0.1%
3235 1
< 0.1%
3216 1
< 0.1%
3114 1
< 0.1%
3071 1
< 0.1%

Cum Inns Total
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct108
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.505817
Minimum0
Maximum120
Zeros438
Zeros (%)4.3%
Negative0
Negative (%)0.0%
Memory size79.4 KiB
2024-10-28T12:19:55.869607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median7
Q316
95-th percentile43
Maximum120
Range120
Interquartile range (IQR)13

Descriptive statistics

Standard deviation14.712554
Coefficient of variation (CV)1.1764568
Kurtosis6.9899119
Mean12.505817
Median Absolute Deviation (MAD)5
Skewness2.3335303
Sum126834
Variance216.45924
MonotonicityNot monotonic
2024-10-28T12:19:56.362580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 904
 
8.9%
2 842
 
8.3%
3 751
 
7.4%
4 742
 
7.3%
5 566
 
5.6%
6 505
 
5.0%
7 489
 
4.8%
0 438
 
4.3%
8 394
 
3.9%
9 328
 
3.2%
Other values (98) 4183
41.2%
ValueCountFrequency (%)
0 438
4.3%
1 904
8.9%
2 842
8.3%
3 751
7.4%
4 742
7.3%
5 566
5.6%
6 505
5.0%
7 489
4.8%
8 394
3.9%
9 328
 
3.2%
ValueCountFrequency (%)
120 1
< 0.1%
118 1
< 0.1%
116 2
< 0.1%
110 1
< 0.1%
108 1
< 0.1%
106 1
< 0.1%
105 1
< 0.1%
104 1
< 0.1%
103 1
< 0.1%
100 1
< 0.1%

Cum SR
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct5511
Distinct (%)54.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean88.993606
Minimum0
Maximum600
Zeros762
Zeros (%)7.5%
Negative0
Negative (%)0.0%
Memory size79.4 KiB
2024-10-28T12:19:56.679049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q157.0875
median95.83
Q3121.0775
95-th percentile156.5395
Maximum600
Range600
Interquartile range (IQR)63.99

Descriptive statistics

Standard deviation47.144715
Coefficient of variation (CV)0.52975396
Kurtosis2.7955782
Mean88.993606
Median Absolute Deviation (MAD)30.08
Skewness0.17317431
Sum902573.15
Variance2222.6242
MonotonicityNot monotonic
2024-10-28T12:19:57.099929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 762
 
7.5%
100 175
 
1.7%
50 168
 
1.7%
33.33 92
 
0.9%
25 83
 
0.8%
66.66 61
 
0.6%
75 52
 
0.5%
16.67 44
 
0.4%
40 35
 
0.3%
133.33 33
 
0.3%
Other values (5501) 8637
85.2%
ValueCountFrequency (%)
0 762
7.5%
2.5 1
 
< 0.1%
2.94 1
 
< 0.1%
3.57 1
 
< 0.1%
4.17 3
 
< 0.1%
4.44 1
 
< 0.1%
4.76 1
 
< 0.1%
5 4
 
< 0.1%
5.55 2
 
< 0.1%
5.71 1
 
< 0.1%
ValueCountFrequency (%)
600 2
< 0.1%
400 1
 
< 0.1%
350 1
 
< 0.1%
333.33 1
 
< 0.1%
328.85 1
 
< 0.1%
300 1
 
< 0.1%
278.57 1
 
< 0.1%
275 1
 
< 0.1%
266.66 3
< 0.1%
261.53 1
 
< 0.1%

Overs
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct338
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.3126306
Minimum0
Maximum79.4
Zeros3582
Zeros (%)35.3%
Negative0
Negative (%)0.0%
Memory size79.4 KiB
2024-10-28T12:19:57.444619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4
Q313
95-th percentile31
Maximum79.4
Range79.4
Interquartile range (IQR)13

Descriptive statistics

Standard deviation10.831502
Coefficient of variation (CV)1.3030174
Kurtosis3.9693632
Mean8.3126306
Median Absolute Deviation (MAD)4
Skewness1.8332422
Sum84306.7
Variance117.32144
MonotonicityNot monotonic
2024-10-28T12:19:57.859124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3582
35.3%
4 489
 
4.8%
1 368
 
3.6%
2 330
 
3.3%
3 310
 
3.1%
8 303
 
3.0%
6 248
 
2.4%
12 246
 
2.4%
7 234
 
2.3%
5 217
 
2.1%
Other values (328) 3815
37.6%
ValueCountFrequency (%)
0 3582
35.3%
0.1 13
 
0.1%
0.2 7
 
0.1%
0.3 6
 
0.1%
0.4 6
 
0.1%
0.5 10
 
0.1%
1 368
 
3.6%
1.1 9
 
0.1%
1.2 9
 
0.1%
1.3 3
 
< 0.1%
ValueCountFrequency (%)
79.4 1
< 0.1%
74 1
< 0.1%
73.4 1
< 0.1%
73 1
< 0.1%
68.5 1
< 0.1%
68.1 1
< 0.1%
68 1
< 0.1%
67.4 1
< 0.1%
66.5 1
< 0.1%
65 1
< 0.1%

Wkts
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct34
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7388089
Minimum0
Maximum41
Zeros4730
Zeros (%)46.6%
Negative0
Negative (%)0.0%
Memory size79.4 KiB
2024-10-28T12:19:58.218798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q34
95-th percentile11
Maximum41
Range41
Interquartile range (IQR)4

Descriptive statistics

Standard deviation4.10465
Coefficient of variation (CV)1.4986989
Kurtosis6.6156812
Mean2.7388089
Median Absolute Deviation (MAD)1
Skewness2.2224454
Sum27777
Variance16.848152
MonotonicityNot monotonic
2024-10-28T12:19:58.737767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
0 4730
46.6%
1 977
 
9.6%
2 836
 
8.2%
3 736
 
7.3%
4 552
 
5.4%
5 444
 
4.4%
6 365
 
3.6%
7 291
 
2.9%
8 247
 
2.4%
9 196
 
1.9%
Other values (24) 768
 
7.6%
ValueCountFrequency (%)
0 4730
46.6%
1 977
 
9.6%
2 836
 
8.2%
3 736
 
7.3%
4 552
 
5.4%
5 444
 
4.4%
6 365
 
3.6%
7 291
 
2.9%
8 247
 
2.4%
9 196
 
1.9%
ValueCountFrequency (%)
41 1
 
< 0.1%
38 1
 
< 0.1%
33 1
 
< 0.1%
31 1
 
< 0.1%
29 1
 
< 0.1%
28 1
 
< 0.1%
27 7
0.1%
26 7
0.1%
25 4
< 0.1%
24 2
 
< 0.1%

Econ
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct865
Distinct (%)8.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.1384204
Minimum0
Maximum39
Zeros3581
Zeros (%)35.3%
Negative0
Negative (%)0.0%
Memory size79.4 KiB
2024-10-28T12:19:59.119579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median6.09
Q38.13
95-th percentile11.6
Maximum39
Range39
Interquartile range (IQR)8.13

Descriptive statistics

Standard deviation4.4258416
Coefficient of variation (CV)0.86132337
Kurtosis0.70650169
Mean5.1384204
Median Absolute Deviation (MAD)3.24
Skewness0.46078929
Sum52113.86
Variance19.588074
MonotonicityNot monotonic
2024-10-28T12:19:59.425476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3581
35.3%
8 153
 
1.5%
6 149
 
1.5%
7 147
 
1.4%
9 132
 
1.3%
10 92
 
0.9%
11 83
 
0.8%
5 82
 
0.8%
6.5 71
 
0.7%
12 66
 
0.7%
Other values (855) 5586
55.1%
ValueCountFrequency (%)
0 3581
35.3%
0.5 1
 
< 0.1%
0.54 1
 
< 0.1%
1 7
 
0.1%
1.33 1
 
< 0.1%
1.5 2
 
< 0.1%
1.63 1
 
< 0.1%
1.71 1
 
< 0.1%
1.85 1
 
< 0.1%
2 10
 
0.1%
ValueCountFrequency (%)
39 1
 
< 0.1%
36 1
 
< 0.1%
32 2
 
< 0.1%
28 2
 
< 0.1%
27.5 1
 
< 0.1%
27 2
 
< 0.1%
25.2 1
 
< 0.1%
25 2
 
< 0.1%
24 6
0.1%
23 2
 
< 0.1%

Cumulative Overs
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1431
Distinct (%)14.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.767423
Minimum0
Maximum394.2
Zeros2943
Zeros (%)29.0%
Negative0
Negative (%)0.0%
Memory size79.4 KiB
2024-10-28T12:19:59.918283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median10
Q336
95-th percentile125.095
Maximum394.2
Range394.2
Interquartile range (IQR)36

Descriptive statistics

Standard deviation46.353249
Coefficient of variation (CV)1.6113105
Kurtosis10.226952
Mean28.767423
Median Absolute Deviation (MAD)10
Skewness2.8172928
Sum291759.2
Variance2148.6237
MonotonicityNot monotonic
2024-10-28T12:20:00.340182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2943
29.0%
1 312
 
3.1%
4 256
 
2.5%
2 232
 
2.3%
3 196
 
1.9%
8 162
 
1.6%
11 139
 
1.4%
7 138
 
1.4%
6 138
 
1.4%
5 133
 
1.3%
Other values (1421) 5493
54.2%
ValueCountFrequency (%)
0 2943
29.0%
0.1 22
 
0.2%
0.2 10
 
0.1%
0.3 3
 
< 0.1%
0.4 3
 
< 0.1%
0.5 9
 
0.1%
1 312
 
3.1%
1.1 10
 
0.1%
1.2 7
 
0.1%
1.4 15
 
0.1%
ValueCountFrequency (%)
394.2 1
< 0.1%
390.1 1
< 0.1%
379 1
< 0.1%
373.7 1
< 0.1%
368 1
< 0.1%
358.2 1
< 0.1%
351.2 1
< 0.1%
347.3 1
< 0.1%
346.9 1
< 0.1%
344.8 1
< 0.1%

Cumulative Wkts
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct123
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.4939854
Minimum0
Maximum136
Zeros3743
Zeros (%)36.9%
Negative0
Negative (%)0.0%
Memory size79.4 KiB
2024-10-28T12:20:00.691134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q312
95-th percentile43
Maximum136
Range136
Interquartile range (IQR)12

Descriptive statistics

Standard deviation15.949708
Coefficient of variation (CV)1.6799802
Kurtosis10.826702
Mean9.4939854
Median Absolute Deviation (MAD)3
Skewness2.895038
Sum96288
Variance254.39317
MonotonicityNot monotonic
2024-10-28T12:20:01.046957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3743
36.9%
1 673
 
6.6%
2 495
 
4.9%
3 492
 
4.9%
4 395
 
3.9%
5 358
 
3.5%
6 299
 
2.9%
7 268
 
2.6%
9 240
 
2.4%
8 233
 
2.3%
Other values (113) 2946
29.0%
ValueCountFrequency (%)
0 3743
36.9%
1 673
 
6.6%
2 495
 
4.9%
3 492
 
4.9%
4 395
 
3.9%
5 358
 
3.5%
6 299
 
2.9%
7 268
 
2.6%
8 233
 
2.3%
9 240
 
2.4%
ValueCountFrequency (%)
136 1
< 0.1%
135 1
< 0.1%
132 1
< 0.1%
130 1
< 0.1%
128 1
< 0.1%
127 1
< 0.1%
124 1
< 0.1%
122 1
< 0.1%
121 1
< 0.1%
119 2
< 0.1%

Cumulative Runs
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1236
Distinct (%)12.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean211.59712
Minimum0
Maximum3120
Zeros2942
Zeros (%)29.0%
Negative0
Negative (%)0.0%
Memory size79.4 KiB
2024-10-28T12:20:01.348583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median74
Q3266
95-th percentile912.9
Maximum3120
Range3120
Interquartile range (IQR)266

Descriptive statistics

Standard deviation340.81181
Coefficient of variation (CV)1.6106637
Kurtosis11.174738
Mean211.59712
Median Absolute Deviation (MAD)74
Skewness2.9039924
Sum2146018
Variance116152.69
MonotonicityNot monotonic
2024-10-28T12:20:01.617453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2942
29.0%
15 57
 
0.6%
11 54
 
0.5%
22 54
 
0.5%
12 46
 
0.5%
21 43
 
0.4%
17 42
 
0.4%
48 42
 
0.4%
14 40
 
0.4%
16 39
 
0.4%
Other values (1226) 6783
66.9%
ValueCountFrequency (%)
0 2942
29.0%
1 14
 
0.1%
2 13
 
0.1%
3 13
 
0.1%
4 27
 
0.3%
5 29
 
0.3%
6 15
 
0.1%
7 17
 
0.2%
8 33
 
0.3%
9 24
 
0.2%
ValueCountFrequency (%)
3120 1
< 0.1%
2997 1
< 0.1%
2874 1
< 0.1%
2854 1
< 0.1%
2771 1
< 0.1%
2730 1
< 0.1%
2666 1
< 0.1%
2584 1
< 0.1%
2578 1
< 0.1%
2534 1
< 0.1%

Cumulative Inns
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct100
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.2816013
Minimum0
Maximum110
Zeros2942
Zeros (%)29.0%
Negative0
Negative (%)0.0%
Memory size79.4 KiB
2024-10-28T12:20:02.069983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4
Q312
95-th percentile39
Maximum110
Range110
Interquartile range (IQR)12

Descriptive statistics

Standard deviation13.828359
Coefficient of variation (CV)1.4898679
Kurtosis7.9363789
Mean9.2816013
Median Absolute Deviation (MAD)4
Skewness2.5194531
Sum94134
Variance191.22352
MonotonicityNot monotonic
2024-10-28T12:20:02.454597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2942
29.0%
1 826
 
8.1%
2 621
 
6.1%
3 537
 
5.3%
4 490
 
4.8%
5 412
 
4.1%
6 345
 
3.4%
8 296
 
2.9%
7 291
 
2.9%
9 259
 
2.6%
Other values (90) 3123
30.8%
ValueCountFrequency (%)
0 2942
29.0%
1 826
 
8.1%
2 621
 
6.1%
3 537
 
5.3%
4 490
 
4.8%
5 412
 
4.1%
6 345
 
3.4%
7 291
 
2.9%
8 296
 
2.9%
9 259
 
2.6%
ValueCountFrequency (%)
110 1
 
< 0.1%
107 1
 
< 0.1%
105 2
< 0.1%
102 2
< 0.1%
98 1
 
< 0.1%
97 1
 
< 0.1%
96 1
 
< 0.1%
95 2
< 0.1%
93 1
 
< 0.1%
91 3
< 0.1%

Cumulative Econ
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct2464
Distinct (%)24.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.103907
Minimum0
Maximum60
Zeros2942
Zeros (%)29.0%
Negative0
Negative (%)0.0%
Memory size79.4 KiB
2024-10-28T12:20:02.816098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median17.666667
Q323.724306
95-th percentile31
Maximum60
Range60
Interquartile range (IQR)23.724306

Descriptive statistics

Standard deviation11.297759
Coefficient of variation (CV)0.74800247
Kurtosis-1.0708861
Mean15.103907
Median Absolute Deviation (MAD)8.0416667
Skewness-0.10865802
Sum153183.82
Variance127.63937
MonotonicityNot monotonic
2024-10-28T12:20:03.148222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2942
29.0%
24 96
 
0.9%
19 90
 
0.9%
15 89
 
0.9%
17 86
 
0.8%
21 85
 
0.8%
16 85
 
0.8%
22 80
 
0.8%
14 79
 
0.8%
23 74
 
0.7%
Other values (2454) 6436
63.5%
ValueCountFrequency (%)
0 2942
29.0%
1 14
 
0.1%
2 14
 
0.1%
2.5 2
 
< 0.1%
2.666666667 1
 
< 0.1%
3 13
 
0.1%
4 26
 
0.3%
4.5 4
 
< 0.1%
4.714285714 2
 
< 0.1%
5 33
 
0.3%
ValueCountFrequency (%)
60 2
< 0.1%
58 1
< 0.1%
57 1
< 0.1%
55 1
< 0.1%
54 1
< 0.1%
52 1
< 0.1%
51 1
< 0.1%
50 1
< 0.1%
49.33333333 1
< 0.1%
49 1
< 0.1%

Dis
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct71
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.2695721
Minimum0
Maximum86
Zeros1858
Zeros (%)18.3%
Negative0
Negative (%)0.0%
Memory size79.4 KiB
2024-10-28T12:20:03.429786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q38
95-th percentile24
Maximum86
Range86
Interquartile range (IQR)7

Descriptive statistics

Standard deviation8.5248813
Coefficient of variation (CV)1.359723
Kurtosis9.9643218
Mean6.2695721
Median Absolute Deviation (MAD)3
Skewness2.6971931
Sum63586
Variance72.673601
MonotonicityNot monotonic
2024-10-28T12:20:03.704018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1858
18.3%
1 1498
14.8%
2 1079
10.6%
3 876
8.6%
4 700
 
6.9%
5 559
 
5.5%
6 451
 
4.4%
7 389
 
3.8%
8 331
 
3.3%
9 253
 
2.5%
Other values (61) 2148
21.2%
ValueCountFrequency (%)
0 1858
18.3%
1 1498
14.8%
2 1079
10.6%
3 876
8.6%
4 700
 
6.9%
5 559
 
5.5%
6 451
 
4.4%
7 389
 
3.8%
8 331
 
3.3%
9 253
 
2.5%
ValueCountFrequency (%)
86 1
< 0.1%
82 1
< 0.1%
79 1
< 0.1%
74 1
< 0.1%
69 1
< 0.1%
67 1
< 0.1%
65 1
< 0.1%
64 1
< 0.1%
63 1
< 0.1%
61 1
< 0.1%

Ct
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct63
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.9727864
Minimum0
Maximum67
Zeros1880
Zeros (%)18.5%
Negative0
Negative (%)0.0%
Memory size79.4 KiB
2024-10-28T12:20:04.036850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q38
95-th percentile22
Maximum67
Range67
Interquartile range (IQR)7

Descriptive statistics

Standard deviation7.9421442
Coefficient of variation (CV)1.3297218
Kurtosis8.398079
Mean5.9727864
Median Absolute Deviation (MAD)3
Skewness2.5466272
Sum60576
Variance63.077654
MonotonicityNot monotonic
2024-10-28T12:20:04.442518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1880
18.5%
1 1522
15.0%
2 1106
10.9%
3 868
8.6%
4 714
 
7.0%
5 556
 
5.5%
6 439
 
4.3%
7 415
 
4.1%
8 334
 
3.3%
9 255
 
2.5%
Other values (53) 2053
20.2%
ValueCountFrequency (%)
0 1880
18.5%
1 1522
15.0%
2 1106
10.9%
3 868
8.6%
4 714
 
7.0%
5 556
 
5.5%
6 439
 
4.3%
7 415
 
4.1%
8 334
 
3.3%
9 255
 
2.5%
ValueCountFrequency (%)
67 1
 
< 0.1%
62 1
 
< 0.1%
61 1
 
< 0.1%
59 3
< 0.1%
58 3
< 0.1%
57 1
 
< 0.1%
56 1
 
< 0.1%
55 3
< 0.1%
54 1
 
< 0.1%
53 2
< 0.1%

St
Real number (ℝ)

ZEROS 

Distinct24
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.29678564
Minimum0
Maximum25
Zeros9333
Zeros (%)92.0%
Negative0
Negative (%)0.0%
Memory size79.4 KiB
2024-10-28T12:20:04.741223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum25
Range25
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.4417211
Coefficient of variation (CV)4.857786
Kurtosis82.570561
Mean0.29678564
Median Absolute Deviation (MAD)0
Skewness7.8562612
Sum3010
Variance2.0785598
MonotonicityNot monotonic
2024-10-28T12:20:04.978978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0 9333
92.0%
1 253
 
2.5%
2 165
 
1.6%
3 100
 
1.0%
4 69
 
0.7%
5 55
 
0.5%
6 39
 
0.4%
9 28
 
0.3%
7 26
 
0.3%
8 22
 
0.2%
Other values (14) 52
 
0.5%
ValueCountFrequency (%)
0 9333
92.0%
1 253
 
2.5%
2 165
 
1.6%
3 100
 
1.0%
4 69
 
0.7%
5 55
 
0.5%
6 39
 
0.4%
7 26
 
0.3%
8 22
 
0.2%
9 28
 
0.3%
ValueCountFrequency (%)
25 1
 
< 0.1%
24 1
 
< 0.1%
23 1
 
< 0.1%
22 4
< 0.1%
21 2
< 0.1%
20 1
 
< 0.1%
18 2
< 0.1%
17 1
 
< 0.1%
15 2
< 0.1%
14 4
< 0.1%

D/I
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct899
Distinct (%)8.9%
Missing4
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean0.33700448
Minimum0
Maximum4
Zeros1854
Zeros (%)18.3%
Negative0
Negative (%)0.0%
Memory size79.4 KiB
2024-10-28T12:20:05.335813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.13333333
median0.29545455
Q30.5
95-th percentile0.89392857
Maximum4
Range4
Interquartile range (IQR)0.36666667

Descriptive statistics

Standard deviation0.2969881
Coefficient of variation (CV)0.88125863
Kurtosis9.1919938
Mean0.33700448
Median Absolute Deviation (MAD)0.17174976
Skewness1.9015685
Sum3416.5514
Variance0.088201934
MonotonicityNot monotonic
2024-10-28T12:20:05.696613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1854
 
18.3%
0.5 533
 
5.3%
0.3333333333 530
 
5.2%
0.25 385
 
3.8%
0.2 310
 
3.1%
1 257
 
2.5%
0.1666666667 235
 
2.3%
0.6666666667 194
 
1.9%
0.4 191
 
1.9%
0.1428571429 159
 
1.6%
Other values (889) 5490
54.1%
ValueCountFrequency (%)
0 1854
18.3%
0.0243902439 1
 
< 0.1%
0.03125 1
 
< 0.1%
0.03225806452 1
 
< 0.1%
0.03333333333 2
 
< 0.1%
0.03703703704 1
 
< 0.1%
0.03846153846 3
 
< 0.1%
0.04166666667 2
 
< 0.1%
0.04347826087 2
 
< 0.1%
0.04545454545 2
 
< 0.1%
ValueCountFrequency (%)
4 1
 
< 0.1%
3 6
 
0.1%
2.5 1
 
< 0.1%
2 22
0.2%
1.833333333 1
 
< 0.1%
1.8 1
 
< 0.1%
1.75 1
 
< 0.1%
1.714285714 1
 
< 0.1%
1.685714286 1
 
< 0.1%
1.673469388 1
 
< 0.1%

Interactions

2024-10-28T12:19:36.662295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:17:22.254332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:17:28.066719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:17:33.999603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:17:43.581018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:17:53.399951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:17:59.654416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:18:06.415781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:18:16.662125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:18:24.092077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:18:28.842128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:18:34.435338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:18:39.587849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:18:45.444091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:18:51.626645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:18:57.213813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:19:02.658213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:19:08.005409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:19:14.106036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:19:20.163890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:19:27.157155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:19:37.046881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:17:22.773894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:17:28.332855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:17:34.413757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:17:44.100855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:17:53.782849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:17:59.934712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:18:06.780801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:18:17.059053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:18:24.306731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:18:29.297996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:18:34.655703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:18:39.811387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:18:45.722475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:18:51.864002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:18:57.507735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:19:02.877395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:19:08.295704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:19:14.338833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:19:20.445846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:19:27.472151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:19:37.579634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:17:23.047816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:17:28.568597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:17:34.956707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:17:44.514011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:17:54.095354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:18:00.301811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:18:07.299664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:18:17.407845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:18:24.516406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:18:29.536476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:18:34.849754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:18:40.038810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:18:46.122270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:18:52.146546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:18:57.814683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:19:03.109020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:19:08.562478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:19:14.605999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:19:20.928100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:19:27.882642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:19:38.014679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:17:23.366161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:17:28.805594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:17:35.343617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:17:44.985381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:17:54.410225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:18:00.664706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:18:07.939474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:18:18.063293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:18:24.731249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:18:29.795445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:18:35.050332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:18:40.268252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:18:46.377679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:18:52.423449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:18:58.043363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:19:03.396735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:19:08.807798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:19:14.915277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:19:21.300904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2024-10-28T12:18:26.948978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:18:32.348125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:18:37.402546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:18:43.096318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:18:49.184051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:18:55.015542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:19:00.689745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:19:05.743149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:19:11.764443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:19:17.771972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:19:24.085274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:19:32.237153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:19:42.027358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:17:26.140661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:17:31.827853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:17:40.165729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:17:50.250903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:17:57.874671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:18:04.280325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:18:13.852817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:18:21.911263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:18:27.203883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:18:32.582752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:18:37.635873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:18:43.545711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:18:49.412998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:18:55.223166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:19:00.920234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:19:06.023880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:19:12.085148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:19:18.083797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:19:24.272888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:19:32.813264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:19:42.357985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:17:26.363054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:17:32.103631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:17:40.604385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:17:50.713959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:17:58.139221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:18:04.559322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:18:14.223526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:18:22.254372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:18:27.463385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:18:32.838584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:18:37.884669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:18:43.903647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:18:49.651970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:18:55.429159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:19:01.177442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:19:06.340754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:19:12.318847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:19:18.383631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:19:24.660975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:19:33.341692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:19:42.650379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:17:26.561843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:17:32.328630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:17:41.003672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:17:51.147758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:17:58.360364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:18:04.790024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:18:14.667365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:18:22.602982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:18:27.686187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:18:33.067986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:18:38.085246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:18:44.178478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:18:49.868527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:18:55.728719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:19:01.420326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:19:06.626253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:19:12.547540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:19:18.679139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:19:24.878197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:19:33.799629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:19:42.983093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:17:26.792855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:17:32.734573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:17:41.476996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:17:51.540768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:17:58.570567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:18:05.055928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:18:15.084588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:18:22.909119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:18:27.904663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:18:33.294444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:18:38.299092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:18:44.434991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:18:50.212885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:18:56.011554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:19:01.628872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:19:06.865363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:19:12.931451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:19:18.982177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:19:25.237175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:19:34.414967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:19:43.311798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:17:27.031488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:17:32.944197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:17:41.915246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:17:52.007195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:17:58.783725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:18:05.307534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:18:15.525840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:18:23.202299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:18:28.087034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:18:33.556279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:18:38.724126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:18:44.724276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:18:50.500784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:18:56.261644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:19:01.826137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:19:07.107162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:19:13.190785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:19:19.242665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:19:25.879216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:19:34.794589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:19:43.780357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:17:27.399270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:17:33.183024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:17:42.479119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:17:52.547533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:17:59.113087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:18:05.604570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:18:15.923257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:18:23.515526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:18:28.283588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:18:33.835003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:18:38.981539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:18:45.005478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:18:50.859142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:18:56.521576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:19:02.093587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:19:07.363470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:19:13.445804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:19:19.577974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:19:26.252466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:19:35.235189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:19:44.104177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:17:27.729425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:17:33.483672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:17:43.050539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:17:52.900315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:17:59.397473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:18:06.004399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:18:16.332858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:18:23.793333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:18:28.592432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:18:34.157789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:18:39.322227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:18:45.227932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:18:51.187350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:18:56.883486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:19:02.333966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:19:07.670715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:19:13.679656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:19:19.862929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:19:26.693862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:19:36.193565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-10-28T12:20:06.003689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
AveCtCum AveCum Inns TotalCum Runs TotalCum SRCumulative EconCumulative InnsCumulative OversCumulative RunsCumulative WktsD/IDisEconInnsMatOversRunsSRSeasonStWkts
Ave1.0000.3570.8430.4990.7200.701-0.318-0.144-0.177-0.179-0.1620.2880.360-0.2450.6120.366-0.1930.9060.7920.0350.147-0.166
Ct0.3571.0000.4550.8080.7060.378-0.0650.3010.2650.2600.2800.6900.996-0.1090.4350.4460.0540.4330.3370.0840.2470.075
Cum Ave0.8430.4551.0000.6100.8550.757-0.344-0.129-0.169-0.170-0.1520.3310.458-0.2860.5510.294-0.2580.7980.6610.0420.169-0.222
Cum Inns Total0.4990.8080.6101.0000.9060.503-0.1450.2450.1960.1970.2040.3270.805-0.1620.6400.501-0.0330.6200.4320.0970.202-0.013
Cum Runs Total0.7200.7060.8550.9061.0000.693-0.2950.033-0.017-0.016-0.0030.3700.706-0.2640.6690.432-0.1950.7990.5930.0580.218-0.163
Cum SR0.7010.3780.7570.5030.6931.000-0.221-0.070-0.097-0.095-0.0810.2830.378-0.1810.4800.273-0.1450.6810.8150.0390.112-0.119
Cumulative Econ-0.318-0.065-0.344-0.145-0.295-0.2211.0000.7300.7750.8040.716-0.226-0.0860.748-0.238-0.0040.739-0.345-0.1730.068-0.3250.642
Cumulative Inns-0.1440.301-0.1290.2450.033-0.0700.7301.0000.9930.9900.956-0.0770.2760.607-0.0040.2440.818-0.131-0.0350.087-0.3220.737
Cumulative Overs-0.1770.265-0.1690.196-0.017-0.0970.7750.9931.0000.9950.966-0.0950.2400.609-0.0400.2240.846-0.170-0.0580.076-0.3250.767
Cumulative Runs-0.1790.260-0.1700.197-0.016-0.0950.8040.9900.9951.0000.956-0.1020.2360.643-0.0420.2160.835-0.172-0.0570.070-0.3250.751
Cumulative Wkts-0.1620.280-0.1520.204-0.003-0.0810.7160.9560.9660.9561.000-0.0720.2570.536-0.0310.2340.821-0.156-0.0450.075-0.2990.810
D/I0.2880.6900.3310.3270.3700.283-0.226-0.077-0.095-0.102-0.0721.0000.706-0.2010.2510.181-0.1410.3060.2470.0470.366-0.109
Dis0.3600.9960.4580.8050.7060.378-0.0860.2760.2400.2360.2570.7061.000-0.1290.4370.4430.0320.4360.3370.0770.3120.056
Econ-0.245-0.109-0.286-0.162-0.264-0.1810.7480.6070.6090.6430.536-0.201-0.1291.000-0.156-0.0040.638-0.251-0.1310.027-0.3070.477
Inns0.6120.4350.5510.6400.6690.480-0.238-0.004-0.040-0.042-0.0310.2510.437-0.1561.0000.7970.0170.8450.4830.0950.1430.030
Mat0.3660.4460.2940.5010.4320.273-0.0040.2440.2240.2160.2340.1810.443-0.0040.7971.0000.3440.5830.3160.1330.0810.348
Overs-0.1930.054-0.258-0.033-0.195-0.1450.7390.8180.8460.8350.821-0.1410.0320.6380.0170.3441.000-0.164-0.0770.058-0.3120.921
Runs0.9060.4330.7980.6200.7990.681-0.345-0.131-0.170-0.172-0.1560.3060.436-0.2510.8450.583-0.1641.0000.7260.0380.171-0.137
SR0.7920.3370.6610.4320.5930.815-0.173-0.035-0.058-0.057-0.0450.2470.337-0.1310.4830.316-0.0770.7261.0000.0440.098-0.056
Season0.0350.0840.0420.0970.0580.0390.0680.0870.0760.0700.0750.0470.0770.0270.0950.1330.0580.0380.0441.0000.0380.077
St0.1470.2470.1690.2020.2180.112-0.325-0.322-0.325-0.325-0.2990.3660.312-0.3070.1430.081-0.3120.1710.0980.0381.000-0.270
Wkts-0.1660.075-0.222-0.013-0.163-0.1190.6420.7370.7670.7510.810-0.1090.0560.4770.0300.3480.921-0.137-0.0560.077-0.2701.000

Missing values

2024-10-28T12:19:44.726397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-10-28T12:19:45.784171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

player_idPlayerCountrySeasonMatInnsRunsSRAveCum AveCum Runs TotalCum Inns TotalCum SROversWktsEconCumulative OversCumulative WktsCumulative RunsCumulative InnsCumulative EconDisCtStD/I
01d45c01aA AndrewsSwitzerland2021/2231.00.00.000.000.000.01.00.0010.04.06.110.04.061.03.020.3333334.04.00.01.333333
11d45c01aA AndrewsSwitzerland202243.034.085.0017.0012.7534.04.042.501.00.021.011.04.082.04.020.5000007.07.00.01.000000
2321be7e3A AshokNew Zealand202310.00.00.000.000.000.00.00.004.01.07.04.01.028.01.028.0000000.00.00.00.000000
358c2fac4A AthanazeWest Indies202444.075.0122.9525.0025.0075.04.0122.950.00.00.00.00.00.00.00.0000002.02.00.00.500000
46ef13460A BalbirnieIreland201574.077.098.7119.2519.2577.04.098.710.00.00.00.00.00.00.00.0000001.01.00.00.200000
56ef13460A BalbirnieIreland2015/1633.07.043.752.3312.0084.07.071.230.00.00.00.00.00.00.00.0000003.03.00.00.375000
66ef13460A BalbirnieIreland201866.0105.0129.6217.5014.54189.013.090.690.00.00.00.00.00.00.00.0000005.05.00.00.357143
76ef13460A BalbirnieIreland2018/1966.0177.0143.9029.5019.26366.019.0104.000.00.00.00.00.00.00.00.0000007.07.00.00.350000
86ef13460A BalbirnieIreland201944.0157.0184.7052.3325.01523.023.0120.140.00.00.00.00.00.00.00.00000010.010.00.00.416667
96ef13460A BalbirnieIreland2019/201716.0422.0122.3128.1326.29945.039.0120.500.00.00.00.00.00.00.00.00000016.016.00.00.390244
player_idPlayerCountrySeasonMatInnsRunsSRAveCum AveCum Runs TotalCum Inns TotalCum SROversWktsEconCumulative OversCumulative WktsCumulative RunsCumulative InnsCumulative EconDisCtStD/I
10132dca8273dZubaidi ZulkifleMalaysia20221010.0252.0182.6028.0021.83365.018.0160.060.00.00.000.00.00.00.00.0000007.07.00.00.388889
10133dca8273dZubaidi ZulkifleMalaysia2022/231111.0321.0140.7832.1025.73686.029.0155.240.00.00.000.00.00.00.00.00000016.016.00.00.571429
10134dca8273dZubaidi ZulkifleMalaysia20231413.0234.0148.1018.0023.34920.042.0153.810.00.00.000.00.00.00.00.00000022.022.00.00.523810
10135dca8273dZubaidi ZulkifleMalaysia2023/2499.0125.0117.9213.8821.671045.051.0147.830.00.00.000.00.00.00.00.00000024.024.00.00.470588
10136dca8273dZubaidi ZulkifleMalaysia202488.0138.0117.9419.7121.401183.059.0143.560.00.00.000.00.00.00.00.00000028.028.00.00.474576
101379124aa4cZuhaib ZubairUnited Arab Emirates2023/2421.013.0144.4413.0013.0013.01.0144.446.00.08.006.00.048.02.024.0000000.00.00.00.000000
1013822ae4973Zuhair MuhammadSaudi Arabia2023/2422.07.043.753.503.507.02.043.750.00.00.000.00.00.00.00.0000000.00.00.00.000000
101392d46e8edZulqarnain HaiderSpain201941.00.00.000.000.000.01.00.0010.03.05.3010.03.053.04.013.2500001.01.00.00.250000
101402d46e8edZulqarnain HaiderSpain2019/2010.00.00.000.000.000.01.00.003.00.02.3313.03.060.05.012.0000001.01.00.00.200000
101412d46e8edZulqarnain HaiderSpain202263.08.066.664.003.008.04.022.2215.05.06.0628.08.0151.011.013.7272731.01.00.00.090909

Duplicate rows

Most frequently occurring

player_idPlayerCountrySeasonMatInnsRunsSRAveCum AveCum Runs TotalCum Inns TotalCum SROversWktsEconCumulative OversCumulative WktsCumulative RunsCumulative InnsCumulative EconDisCtStD/I# duplicates
03a592d0dD SamarawickramaItaly2022/2333.015.055.555.005.0015.03.055.550.00.00.00.00.00.00.00.00.00.00.00.000004
13d5d51a5T MarumaniZimbabwe20211111.0118.092.1810.7210.72118.011.092.180.00.00.00.00.00.00.00.00.00.00.00.000004
23d5d51a5T MarumaniZimbabwe202222.041.0105.1220.5012.22159.013.098.650.00.00.00.00.00.00.00.00.00.00.00.000004
33d5d51a5T MarumaniZimbabwe2022/2333.011.061.113.6610.62170.016.086.140.00.00.00.00.00.00.00.01.01.00.00.062504
43d5d51a5T MarumaniZimbabwe2023/2488.0123.0132.2517.5712.94293.024.097.670.00.00.00.00.00.00.00.03.03.00.00.125004
53d5d51a5T MarumaniZimbabwe202477.0120.0104.3417.1413.89413.031.099.000.00.00.00.00.00.00.00.05.05.00.00.161294
63d5d51a5T MarumaniZimbabwe2024/2555.0216.0222.6854.0019.46629.036.0119.610.00.00.00.00.00.00.00.09.07.02.00.250004
767a9fe72T MarumaniZimbabwe20211111.0118.092.1810.7210.72118.011.092.180.00.00.00.00.00.00.00.00.00.00.00.000004
867a9fe72T MarumaniZimbabwe202222.041.0105.1220.5012.22159.013.098.650.00.00.00.00.00.00.00.00.00.00.00.000004
967a9fe72T MarumaniZimbabwe2022/2333.011.061.113.6610.62170.016.086.140.00.00.00.00.00.00.00.01.01.00.00.062504